Dynamics of Market Structure

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Dynamics of Market Structure and

Competitiveness of the Banking Sector inIndia and its Impact on Output and

Prices of Banking Services

Kaushik Bhattacharya and Abhiman Das*

The paper examines the nature and the extent of changes in the market concentration

in the Indian banking sector and their possible implications on prices and output of bankingservices. The first part of the paper attempts to measure market concentration in banking

in India in alternative ways from 1989-90 to 2000-01. In contrast to earlier empirical

applications on banking, it focuses on both static and dynamic measures of market

concentration. The paper finds a strong evidence of change in the market structure in

banking in India. Interestingly, resul ts reveal that a major part of the change in market

structure occurred during the early 1990s. Despite a spate of mergers during the late 1990s,

market concentration was not significantly affected. It is also observed that the different

concentration ratios rank the changes similarly over time.

The second part of the paper analyses the possible impact of changes in banking

market structure on prices and output of this sector during the same period. It is

demonstrated that measurement problem of real output pertaining to banking sector in the

national income data could be severe. The implied inflation as obtained through the GDPdeflator for the banking sector in India led to unbelievable measures of inflation for banking

services, casting some doubt on the methodology adopted. Alternatively, proxy price

measures based on the spread appear to be more consistent with the changes in market

structure in India during the late 1990s. The paper argues that the favourable market

structure in India could be one important factor that led to a reduction in the prices of

banking services after the administered interest regime was lifted.

Reserve Bank of India Occasional Papers

Vol. 24, No. 3, Winter 2003

* The authors are working as Assistant Advisers in the Monetary Policy Department andDepartment of Statistical Analysis and Computer Services, respectively. The authors wish to

express their sincere thanks to Prof. Esfandiar Maasoumi, Department of Economics, Southern

Methodist University, Dallas, Texas, and Prof. Mark Flannery, University of Florida, Department

of Finance, Gainesville, for their insightful comments, which led to substantial improvement of

the exposition of an earlier draft. The views expressed in this paper are the authors own. The

authors bear full responsibility for any errors that remain.

JEL Classification : D40, G21, L11, L89

Ke y Words : Concentration, Competition, Banking


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124 RESERVE BANK OF INDIA OCCASIONAL PAPERS

Introduction

The role of competition in ushering economic efficiency has been

extensively examined in the literature. In view of globalisation and

renewed interest in shaping appropriate competition policies in many

countries, the issue has once again become germane (Neumann,

2001). A major requirement for enhancing competition in an economy

is the removal or minimisation of entry barriers. An important source

of removing them is to ensure the availability of cheap finances, which

inter alia, is easier to meet in the presence of a thriving and

competitive banking sector. Theoretical results demonstrate that

monopolistic market power of banks raises the opportunity costs ofcapital and thus, tends to make financing more expensive (Smith,

1998). Lack of adequate competition in banking could thus, adversely

affect economic development.

To analyse competitiveness in any sector, an in-depth analysis

of the structure of the market is essential. While highly concentrated

markets do not necessarily imply lack of competitive behaviour, it is

generally agreed that market concentration is one of the most

important determinants of competitiveness (Nathan and Neavel,

1989). For banking sector, the relationship between marketconcentration and competitiveness has been examined in detail for

many countries and the results indicated that a high concentration

tends to reduce competitiveness in this sector (Gilbert, 1984). Most

of the empirical evidences in the literature are, however, based on

developed economies. The financial structures in many developing

countries being sharply different from the developed ones, it is

necessary to examine to what extent the established empirical findings

in the developed economies apply to these countries, especially in

an environment where financial structures are undergoing rapid and

swift changes.

This paper examines the nature and the extent of changes in the

structure of banking in India during the 1990s and analyses the

possible impact of these changes on prices and output of banking

services during the same period. The concepts of price and real output


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DYNAMICS OF MARKET STRUCTURE 125

in the banking sector being fuzzy, an analytical discussion on theseaspects has also been attempted. Currently, a detailed examination

of these issues is relevant because the economic reforms in India

during the 1990s ushered in phenomenal changes in the Indian

banking sector. The new regime, in sharp contrast to the earlier regime

that thrived on banking through public sector, is perceived as more

accommodative towards competition. A fundamental change in this

context during the second half of the 1990s had been the liberalisation

of the earlier administered interest rate regime. Besides that, other

significant policy measures included reduction in reserve ratio,

relaxation of quantitative restrictions assets/liability composition and

removal of some of the major barriers to entry into the financial

system. The new policy framework also entailed considerable

institutional reforms, including new laws and regulations governing

the financial sector, the restructuring and privatisation of banks, and

the adoption of indirect instruments of monetary policy. In the current

regime, banks enjoy almost full freedom in pricing their products.

Furthermore, a spate of new entries of private Indian and foreign

banks and mergers among some of the existing players during the

second half of the 1990s is expected to usher in significant changes

in the structure of the banking sector in India.

The changes in the market structure of firms could be examined

through alternative measures. Recent survey of Bikker and Haaf

(2001a) lists 10 such measures proposed and used in the literature.

Among these, the more popularly used ones are k-Bank Concentration

Ratios and Herfindahl-Hirschman Index (HHI). The Lorenz Ratio

(Gini Coefficient), a popular measure in the literature on income

inequality, is also used to measure industrial concentration. In India,

some of these measures have been used by the official agencies to

address similar problems. 1 It may, however, be noted that the scope

of these popular measures is somewhat limited. For example, theHHI and the Gini coefficient are based on the variance of market

shares. So far as market concentration is concerned, policy makers

are in most cases not interested in the variance per se, but at the tails

of the distribution of market shares. Although some of the measures


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listed by Bikker and Haaf (2001a) attempt to address these problems,all of them ignore the inherent dynamics associated in this process.

To analyse competitiveness in an industry, specification of a full

dynamic framework is necessary to gain sufficient hold on the market

in the long run, while firms may initiate price wars, resulting in

apparently misleading changes in the short-run concentration profiles.

Although the dynamic aspects of concentration have been addressed

in the literature, earlier studies focussed primarily on a descriptive

analysis of the changes in indices of concentration from year to year

in specific industries and related it to competitiveness measured

in alternative ways.

Recent advances in the literature have, however, explicitly

focussed on the dynamic aspects of concentration measures.

Borrowing concepts from the related literature on income mobility,

Maasoumi and Slottje (2002) have classified measures of industrial

concentration based on generalised entropies, obtained asymptotic

distributions for these measures and applied them on the US steel

industries. Empirical results reveal that the incorporation of the

dynamic aspects could lead to changes in inferences drawn from more

traditional static measures. As this development is a nascent one, the

empirical relevance of these developments in the banking sector isyet to be examined.

So far as banking sector is concerned, our study is different from

the earlier studies in two respects. First, we examine the changes in

concentration in the banking sector in India in both static and dynamic

framework and compare them empirically. While the static framework

employs standard measures of concentration, in the dynamic

frameworks, we measure these changes through generalised entropy

measures as developed by Maasoumi (1986) and Maasoumi and

Zandvakili (1990). Wherever possible, results are compared to thoseobtained for other countries. Second, while examining the

implications of changes in the concentration profiles on

competitiveness and on the prices and output of the banking sector,

we demonstrate that standard measures of prices and output as per


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the national accounts statistics could provide a distorted picture. Weargue that alternative proxies of price based on the spread between

the lending and the deposit rates appear to be more consistent with

the changes in the concentration profiles of banks in India during

this period.

The plan of the paper is as follows: Section I presents a brief

review of literature on measuring concentration, with special

reference to dynamic measures of concentration. Section II describes

the empirical evidence on changes in the structure of banking sector

in India. SectionIII attempts to analyse the possible impact of these

changes on prices and quantities of the financial intermediation

services. Finally, Section IV concludes the paper with some critical

comments, focussing on policy aspects.

Section I

A Brief Review of Literature

So far as measurement of market concentration is concerned,

many of the existing results on income inequality could be readily

translated. Drawing analogies from the literature on incomeinequality, the inequality in the share of sales (or output or share of

industry employees) of individual firms in an industry has been

specified as appropriate empirical measure of market concentration.

These measures have been estimated and related them to

competitiveness measured in alternative ways.

In the income inequality literature, inequality has also been

examined in a dynamic framework. These mobility studies have been

compared to videotapes on inequality as against a spot picture

provided by the static measures. Recently, attempts have been madeto translate the framework to measure market concentration.

Accordingly, Subsection I.1 reviews the static measures of

concentration and Subsection I.2 does that for the dynamic measures.


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I.1 Static Measures of Concentration

In the static inequality literature, different inequality measures

do not necessarily imply the same ordering of distributions. Either

explicitly or implicitly, almost all these measures or orderings are

based on a weighted average of the income (or, wealth) vector of

individuals (or, households). The disagreement occurs in the

specification of the weights. The disagreement is irrelevant, if there

are good reasons to demonstrate the superiority of one measure

over others. The good reasons could be specified in alternative ways.

One way is to identify a few desirable properties that a measure on

inequality should satisfy. Some of these properties are symmetry,continuity, invariance to scalar multiplication, additive

decomposability and satisfaction of transfer principle (Shorrocks,

1984). Another way is to derive an inequality measure or an ordering

from a social welfare function (SWF). The SWF is specified as a

function of the income (or, wealth) vector of all individuals (or,

households). Thus, different income distributions can be ordered

based on the SWF pertaining to them. This approach often involves

specifying an axiomatic structure that such a SWF should satisfy.

Subsequent task involves characterising indices or orderings that

would satisfy such axioms.

Like indices of inequality, different indices of concentration put

different weights over different parts of the distribution of market

shares across firms and may give contradictory evidence. Let there

be n firms in an industry with market shares s1, s

2, , s

n. A simple

but general linear form of an index of industrial concentration (IIC)

is:

where wi (i=1,2, , n) are weights that may or may not sum to unity.Following the taxonomy of Marfels (1971), there could be four broad

classes of weighing schemes: (i) unity to top k firms and zero to the

rest, (ii) individual ranks of firms, (iii) firms own market shares or

their power, and (iv) the negative of the logarithm of market shares.

(1) IIC= =

n

i

iisw1


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The weighing scheme reflects different assessment regarding therelative impact of larger and smaller firms. Depending upon the

weighing scheme, the individual measures may vary, but they may

lead to similar orderings.2

As in the inequality literature, there are two ways to deal with

the problem of lack of robustness with respect to weights. One way

is to report complete rankings through a class of concentration

measures that reflect the sensitivity to concentration in all parts of

the share distribution. Another approach is to consider partial but

uniform orderings that evaluate concentration over a restricted part,

but over a larger class of evaluative functions. Whatever be thestrategy, Maasoumi and Slottje (2002) argue that for transparencys

sake, it is imperative for the policymakers and analysts to declare

the weights they attach to a reduction in concentration over various

parts of a distribution.

The most common measure used in the literature on market

concentration has been a simple concentration index, aggregating such

shares of a few top firms (say, k). These measures for banking firms

are called k-Bank Concentration Ratios. There is no rule for choosing

an appropriate value of k. So, the number of firms included in the

concentration index is an ad hoc and an arbitrary decision. The index

ranges from zero to unity. The index approaches zero for an infinite

number of equally sized banks and it equals unity, if the firms included

in the calculation of the concentration ratio make up the entire

industry.

Another popularly used measure is the Hirfendahl-Hirschman

index (HHI)3 . For n firms in an industry with market shares si,

(i=1,2, ... , n), the HHI is defined as:

HHI can be written as an increasing function of the population

variance of market shares. The more equal the firms size is, the

smaller is the HHI. HHI also satisfies the well known transfers

(2) HHI = =

n

iis

1

2


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property. By definition (1/n)


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(c) The Comprehensive Industrial Concentration Index (CCI) is definedas :

(6)

It is the sum of the proportional share of the leading bank and the

summation of the squares of the proportional sizes of each bank, weighted

by a multiplier, reflecting the proportional size of the rest of the industry.

(d) The Hannah and Kay Index (HKI) is defined as :

(7) and

where a is an elasticity parameter to be specified and intendedto reflect their ideas about changes in concentration as a result of the

entry or exit of banks, and the sales transfer among the different banks

in the market. The freedom to choose a allows for alternative views on:what is the appropriate weighting scheme and for the option to emphasise

either the upper or the lower segment of the bank size distribution.

Therefore, in addition to the distribution of the banks in the market, thevalue of the index is sensitive to the parameter a. For 0 , the indexapproaches the number of banks in the industry, and for , itconverges towards the reciprocal of the market share of the largest bank.

(e) The Hause Indices

i) The multiplicatively modified Hause Index takes the form:

(8) { }( ) ==

n

i

sHHIs

iimiissH

1

))((2 2

,

where HHI is the Herfindahl-Hirschman Index and a is the parametercapturing the degree of collusion.

ii) Hause furthermore proposes the additively adjusted measure of

concentration, which is defined as:

(9) { }( ) = +=n

i iiiiasHHIsssH

1

22 )))(((,

with 1> .

))1(1(2

2

1 i

n

i isssCCI ++= =

)1/(1

1

)( ==

n

i

isHKI 10>


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(f) Entropy Measure

The Entropy measure has its theoretical foundations in information theory

and measures the ex-ante expected information content of a distribution.

It takes the form:

(10) ==n

i iissE

1log

(g) Coefficient of Concentration (CC) is defined as:

(11) C= n/(n-1)*G

Indices (a) to (g) are discussed in detail in Bikker and Haaf

(2001a). It may be noted that some of the indices are based on higher

moments of market shares. For example, the Comprehensive

Concentration Index (CCI) could be associated with the third moment

of market shares. In some cases, they are functions of market shares as

well as the HHI. Some of the measures, in fact, represent broad classes.

The values of a specific measure within that class will depend on specific

values of certain parameters. In an empirical exercise, the choices of the

values of these parameters are often not clear. Researchers typically

specify a set of plausible values of these parameters and examine the

robustness of the obtained results.

Availability of so many indices implies that in any specific

exercise, it is important to specify the underlying axiomatic structure

under which the corresponding index becomes the best index. In their

various incarnations, axiomatic structures identify the generalised entropy

(GE) as an ideal family of indices. For a weighted random vector

X=(X1,,X

n)'with weights w=(w

1,,w

n)'the GE concentration measure

is defined as:

where ( )=

=n

i

iw

w

nXX i

1

1and

=

=n

i

inww

1

1, the weights being the

( ) ( ) 1,0for1)((12)1

1

)1(1

=

=

+

+

n

i

X

X

w

w

n

iiXI


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reciprocal inclusion probabilities. This family includes HHI, varianceof logarithms, square of the coefficient of variation, and for l =0, -1, thisindex converges to the first and second Theil measures of information

respectively:

It has been shown that Theil's second measure (l = -1) provides the

most unambiguous answer to such fundamental questions as: How much

of the overall concentration is due to the concentration within the rt h

group? The groups could be with respect to type of product, technology,

location, or the size itself. These two measures in (13) and (14) were

further studied by Maasoumi and Theil (1979) with a view to determine

their characteristics in terms of the moments of distributions. Let

log(x)=z, E(z)=m, var(z)=s2, g1=E(z-m)3 /s 3 as the skewness, and

g2=E(z-m)4/s4-3 as the kurtosis of the log output distribution. Assuming

the existence of the first four moments and carrying out Nagar type

approximations, they obtained :

(15) I0 = - s2

[1+ 32

sg1- s2

g2

+o(s2

)]

(16) I1

= - s2[1-31 sg

1- s2g2+o(s2)]

When z has a lognormal distribution, both indices equal - (1/2)s2 .HHI can be shown to be a simple function of s2, but not of the highermoments. Thus, it can fail with departures from lognormality. The above

approximate formulae can be used when the underlying distribution is

not known. They allow us to see that positive skewness and leptokurtosisincrease concentration, and that I

0is more sensitive to positive skewness

(high sales/output groups) and fat tails (large extreme sales groups) than I-1

.

As these entropy measures appear more general and relatively easy

to implement, their use in the context of measuring market concentrationhas often been suggested. This is because entropy is shown as a much

richer function of all the moments of a distribution, and more closely

identifies it than any single moment such as variance or HHI.

( ) ( ) ( )[ ]=

=n

i

X

X

X

X

w

wiii

XI

1

0 log)()13(

( ) ( )[ ]=

=n

i

X

X

w

w

n

ii

XI

1

11

log)()14(


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I.2 Dynamic Measures of Concentration

The dynamic measures of concentration emerge from the realisation

that it is misleading to consider states of a market at only single points

in time. Transitory conditions may mislead and become difficult to

disentangle when looking at several periods/situations. It is thus, desirable

to consider market concentration over several periods, and to develop a

dynamic concentration profile, following the concepts of mobility as in

Maasoumi and Zandvakili (1990).

LetXit

denote sales/output of firm i, (i=1,,N), in period t=1,,T.

We denote the vectorXt=(X1t ,X2t ,....,XNt). Let Si=Si (Xi1,, XiT) be thepermanent or aggregate sales of firm i over T periods. Of course,one can define the aggregates over periods 1 to T T, say, and developa mobility profile as T approaches T. Then

(17) S =(S1,, S

n)

is the vector of aggregate sales for a chosen time frame. Following

Maasoumi (1986), the following type of aggregation functions are justi-

fied on the basis that they minimize the generalized entropy distance

between S and all of the T sales distributions:

where at

is the weight attached to sales in period t, Sat=1. The

elasticity of substitution of sales/output across time is constant at a=1/(1+b). The case b= 1 corresponds to perfect inter-temporal substitution,which subsumes Shorrocks analysis for certain weights. This case is

also the most common formulation of the permanent income concept

in economics. In this context, we can think of the concept as permanentoutput, or expected sales.

Mobility is measured as the ratio of long run concentration

occurring when the period of examination is extended, and a measure of

1for

0for)(

1,0for)()18(

1

1

/1

1

==

==

=

=

=

=

it

T

t

t

T

t

it

it

T

t

ti

X

X

XS

it


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short run concentration. The latter may be represented by any one periodof interest, or a weighted average of the single period concentrations.

We might think of this as a notion of competition enhancing mobility,

a welfare theoretic base in favour of large, non-concentrated markets.4

The extension of the time interval is meant to reflect the dynamics and

smooth out the transitory or business cycle effects in the industry.

Shorrocks (1978) proposed the following mobility measures:

where I() is the inequality measure. For convex inequality measuresI(), 0M1 is easily verified when S is the linear permanent outputfunction. For other aggregator functions see Maasoumi and Zandvakili

(1990).A priori, there would be no reason for an analyst to give unequal

weights to different years under study. Nevertheless, Shorrocks (1978)

suggests the ratio of year t income to total income over the T periods as

suitable values for at

s. We consider both weighting schemes here.5

Section II

Empirical Analysis

Compared to many other developing countries, India has an

extensive banking network. Before an empirical analysis, a brief

discussion on the taxonomy and the historical development of the

structure of the Indian banking market would be essential.6 Accordingly,

Subsection II.1 presents such a review in brief. Subsections II.2 and II.3

present results based on static and dynamic measures, respectively.

II.1 Taxonomy and Historical Development

The scheduled banking structure in India consists of banks that are

included in the Second Schedule of the Reserve Bank of India Act, 1934.These scheduled banks are divided in two groups, viz., scheduled

commercial banks and scheduled co-operative banks. This study is

restricted to scheduled commercial banks that account for more than 90

per cent of banking business in India. For analytical purposes, the scheduled

=

=T

t

ttXI

SIM

1

)(

)(1)19(


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commercial banks could be further classified into four groups, viz., publicsector banks, Indian private sector banks, regional rural banks and foreignbanks. Among the public sector banks, official reports generally indicate

results separately for State Bank of India (SBI) and its Associates andOther Nationalized Banks, due to the large size of the former.

So far as banking is concerned, the year 1969 marked a watershed,

during which fourteen major banks in India were nationalised. At thattime, there were 73 scheduled commercial banks in India, of which 15

were foreign banks. Due to strong emphasis on increasing the savingsrate of the economy, the 1970s and 1980s experienced phenomenal

growth in the banking network that spanned the entire country. As aresult, though the absolute number of scheduled commercial banks (other

than regional rural banks) did not increase much (78 as at end-March1990, of which 22 were foreign banks), the number of bank branches

increased from 8,262 in 1969 to 59,752 as at end-March 1990, resultingin a very high annual compound growth rate of 18.8 per cent in deposit

mobilisation, from Rs.4,646 crore as at end-March 1969 to Rs.1,73,515

crore as at end-March 1990. The Indian Government has historicallyundertaken a number of extensive and elaborate policy initiatives to

extend the outreach of formal credit systems to the rural population.One of the major initiatives taken was the establishment of Regional

Rural Banks (RRBs) in 1975. These policy initiatives during 1970-90had a far-reaching impact on the functional reach and geographical spread

of banking in India. However, this period is also characterised bywidespread control, limiting the scope of competition. Interest rates were

strictly administered and had multiple layers. On the lending side, thefocus was on priority sectors. The banking market during this period

was also highly segmented.

Following the balance of payments crisis in India during the early1990s, the earlier regime experienced a radical change. A major change

was to shift away from the earlier administered rates towards marketdetermined ones. On the lending side, the deregulation began in 1994

with emphasis on the development of money, Government securitiesand foreign exchange markets. The conduct of monetary policy also

slowly moved away from the use of direct instruments of monetary

control to indirect measures such as open market operations. Banks were

given freedom to set their Prime Lending Rates and to devise their own


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lending policies. On the liabilities side, the entire gamut of deposit rates except on savings deposits were deregulated, and the banks weregiven freedom to offer different interest rates for different maturities/

size-groups. Interest rates on Government securities were made market-determined. The refinance facility of Reserve Bank of India (RBI) wasalso rationalised and sector specific refinance facilities were

de-emphasised. During 1997, another overriding development with farreaching implications was, however, the reactivation of the Bank Rate,

which was linked to other interest rates including the Reserve Banksrefinance rate. During this period, banks were also permitted to rationalisetheir existing branch network viz., to shift their existing branches within

the same locality, open certain type of specialised branches, convert theexisting non-viable rural branches into satellite offices, etc.

Table 1 presents the movement of select banking indicators during lasttwo decades. It is observed that the decade 1992-02 is marked with

significant increase in the banking business by Indian private banks.While the deposits of Indian public sector banks grew at an annual

compound growth rate of around 15.55 per cent during 1992-02, thesame for Indian private banks grew at an annual compound growth for

around 28.57 per cent. In the case of bank credit also, a similar pattern is

observed.

The liberalisation measures adopted during the beginning of the

study period, attempted to reduce entry barriers by discarding the earlier

licence-permit regime. As a consequence, there were a number of new

entrants in the banking business during this period. Table 2 lists the

Table 1: Movement of Select Banking Indicatorsduring 1982-92 to 1992-02

Bank-groups Growth in number Compound growth Compound growth

of branches of deposits of bank credit

1982-92 1992-02 1982-92 1992-02 1982-92 1992-02

State Bank of India & its

Associates 26.54 7.69 16.18 15.48 14.64 15.32

Nationalised Banks 32.64 3.41 15.15 15.59 13.81 15.23

Regional Rural Banks 60.09 -1.82 27.46 23.07 21.64 16.52

Indian Private Banks -5.87 23.67 15.57 28.57 17.85 28.23

Foreign Banks 12.58 17.93 25.91 13.46 21.53 19.29

Total 35.33 4.80 16.12 16.75 14.83 17.00


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new arrivals of banks in India between 1989-90 and 2000-01chronologically. It is interesting to note that during the first few years,

there were no new arrivals. The early 1990s was the period of

consolidation after the economic debacle following the balance of

payments crisis experienced by India during the year 1990-91. The

arrivals started during early 1994 after the crisis was effectively

tackled and in consequence, the pace of liberalisation in the Indian

financial sector accelerated. Table 2 reveals the arrival of 33 new

banks during this period, among which 24 are of foreign origin. It is

also interesting to note that the arrival of the foreign banks accelerated

during the later period.

Table 2: Entry of New Banks during 1990-2001

Bank Name Date of Ownership Bank Name Date of Ownership

Opening Category Opening Category

Barclays Bank 8/10/90 Foreign Bank Bank of Ceylon 30/10/95 Foreign Bank

Sanwa Bank 20/12/90 Foreign Bank Commerz Bank 1/12/95 Foreign Bank

UTI Bank 28/02/94 Indian Private Siam Commercial 14/12/95 Foreign Bank

Bank Bank

IndusInd Bank 2/04/94 Indian Private Bank International 6/04/96 Foreign Bank

Bank Indonesia

ICICI Bank 17/05/94 Indian Private Arab Bangladesh 6/04/96 Foreign Bank

Bank Bank

ING Bank 1/06/94 Foreign Bank Chinatrust 8/04/96 Foreign Bank

Commercial Bank

Global Trust Bank 6/09/94 Indian Private Cho Hung Bank 6/05/96 Foreign Bank

Bank

Chase Manhattan Bank 21/09/94 Foreign Bank Fuji Bank 20/05/96 Foreign Bank

State Bank of Mauritius 1/11/94 Foreign Bank Krung Thai Bank 6/01/97 Foreign Bank

HDFC Bank 5/01/95 Indian Private Overseas Chinese 31/01/97 Foreign Bank

Bank Bank

Centurion Bank 13/01/95 Indian Private Commercial Bank 12/03/97 Foreign Bank

Bank of Korea

DBS Bank 15/03/95 Foreign Bank Sumitomo Bank 20/06/97 Foreign Bank

Bank of Punjab 5/04/95 Indian Private Hanil Bank 5/07/97 Foreign Bank

Bank

Times Bank 8/06/95 Indian Private Toronto-Dominion 25/10/97 Foreign Bank Bank Bank

Dresdner Bank 21/08/95 Foreign Bank Bank Muscat 9/09/98 Foreign Bank

International

IDBI Bank 28/09/95 Indian Private Morgan Guaranty 24/12/98 Foreign Bank

Bank Trust K. B. C. Bank 15/02/99 Foreign Bank


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The new environment in banking demanded restructuring andreorienting the policy goals of banks. One way to adapt to the new

environment was through mergers. It may be noted that though bank

mergers were common phenomenon in many developed and

developing countries, they were comparatively new in India during

the 1990s.7 Table 3 presents the list of mergers and acquisitions

among the banks. It lists 18 such mergers. Once again, it may be

noted that 10 of the mergers and restructuring took place during the

second half of the 1990s. To understand the nature of these mergers

in detail, the type of merger has also been indicated in Table 3. Most

of the mergers took place either between two private sector banks or

two public sector banks. Among the public sector banks, generally aweak bank had been merged with a strong bank. Thus, if one

considers public sector or private sector as a group, the effect of

merger on bank performance may not be very significant. In one or

Table 3 : Mergers and Acquisitions of Banks: 1985-2002

Name of the merging entity No. of Name of the merged entity Date/Year of

Branches merger

United Industrial Bank 145 Allahabad Bank 31/10/89

Bank of Tamil Nadu 99 Indian Overseas Bank 20/02/90

Bank of Thanjavur 156 Indian Bank 20/02/90

Parur Central Bank 51 Bank of India 20/02/90

Purbachal Bank 40 Central Bank of India 29/08/90

Bank of Karad 48 Bank of India 2/05/92

New Bank of India 591 Punjab National Bank 1993

BCCI (Mumbai) 1 State Bank of India 1993

Kashinath Seth Bank 11 State Bank of India 1/01/96

Bari Doab Bank 10 Oriental Bank of Commerce 8/04/97

Punjab Co-operative Bank 9 Oriental Bank of Commerce 8/04/97

20th Century Finance Centurion Bank 1/01/98

Bareilly Corporation Bank 65 Bank of Baroda 3/06/99

Sikkim Bank 7 Union Bank of India 22/12/99Times Bank 10 HDFC Bank 26/02/00

Bank of Madura 270 ICICI Bank 10/03/01

Sakura Bank 2 Sumitomo Bank 1/04/01

Morgan Gurantee Trust 1 Chase Manhattan Bank 10/11/01


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two cases, it is observed that a non-banking financial company hadbeen merged with a bank.

II.2 Changes in the Market Structure in Indian Banking:

Static Measures

The evidences in Tables 13 reflect the changes in the structure

of Indian banking. We now attempt to examine these changes in detail

by measuring the changes in different concentration indices over the

years. We estimated the measures of concentration at industry as well

as at bank-group level with respect to total assets, total deposits and

total income. However, for the sake of brevity, we have presentedthe values at industry level based on total assets.8

From an analytical point of view, before discussing the trends of

the various concentration measures in the post-reform period, we

present a brief statistical profile of various concentration measures

(Table 4).9 The first impression demonstrates the diverging results

yielded by the various concentration measures when applied to the

same underlying market. Even a short glance reveals the wide spread

in these values. The results show clearly that not only does the range

of possible values differ strongly across the indices, but so do the

values of the indices within this range. For instance, the value ishigh for the CR

kand low for the HHI and Rosenbluth index.

Table 5 presents the trends in various concentration measures

during 1989-90 to 2000-01. Note that as these figures are population

figures (scheduled co-operative banks are excluded, we interpret

scheduled commercial banks as the population), computations of

standard errors are not necessary. In general, concentration indices,

as presented in Table 5, appear to be inversely related to the number

of banks. This is owing to the well-known weakness of concentration

indices, namely, their dependency on the size of the banking market.

The value of the k-bank concentration ratios (for various values of k)always exceeds the value of HHI, since the latter gives less

prominence to the markets shares (the weights again being market

shares) than the former (unit weights). Irrespective of the choice of

the concentration index, measures of concentration have declined in


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the post-reform period. Two different patterns are very clear:

(a) there exists a uniform ordering/trend across various measures, (b)

although reform process reduced concentration in the industry, the speed

of reduction has been noticeably slow. However, the role of financialliberalisation in lowering concentration is clearly established. It is

interesting to note that the major part of the change in the structure

had occurred during the early 1990s. Thus, the spate of mergers during

the late 1990s did not change the market structure significantly.

Table 4: Average Measures of Concentration: 1989-90 to 2000-01Index Range Parameters Typical features Avg. Std. CV

type Value Dev.

GINI 0.736 0.012 1.631

CR1

Takes only large banks 0.250 0.020 7.931

CR2

0 < CRk1 into account; 0.311 0.025 8.160

CR5

arbitrary cut off 0.472 0.032 6.690

CR10

0.628 0.044 6.940

HHI 1/n HHI 1 Considers all banks; sensitive 0.085 0.012 13.835to entrance of new banks

HTI 0 < HTI 1 Emphasis on absolute 0.050 0.005 9.805number of banks

Rosen- 0 < RI 1 Sensitive to changes in 0.044 0.007 15.753bluth the size distribution of

small banks

CCI 0 < CCI 1 Addresses relative dispersion 0.293 0.023 7.810

and absolute magnitude;

suitable for cartel markets

CI 0.745 0.013 1.700

HKI (1/s1)HKIn a = 0.005 Stresses influence small banks 86.880 11.292 12.998

a = 0.25 62.355 8.264 13.253

a = 5 5.686 0.542 9.529

a= 10 Stresses influence large banks 4.688 0.399 8.501

Hause 0 < Hm 1 a= 0.25 Suitable for highly collusive 0.138 0.019 13.481

index marketsa = 1 0.085 0.012 13.947

a = 2 Suitable for not collusive 0.085 0.012 13.837

markets

Entropy 0 E log n Based on expected 3.282 0.152 4.633information content of a

distribution


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To establish the observed first pattern, Table 6 presents product-

moment correlations among various concentration indices in India over

time10 . Results based on almost all the pairs are similar, displaying a

high degree of correlation. The strongest correlations are found between

CR1 and CR2, RI and CR3, CCI and CR1, HHI and CR1. These results

clearly demonstrate that, at least in the Indian context, the behaviour of

various concentration indices is very similar. Thus, our results indicate

that though a host of measures for market concentration are available,an empirical application is unlikely to yield different rankings of a single

economy over time. Our results thus, compliment the results of Bikker

and Haaf (2001a), who did a similar exercise over space. The observed

correlations are, however, not very strong when the measures are based

on either total deposits or total income , indicating that some differences

could exist across the variable, which is used to compute the size

distribution (e.g., asset, deposit and income)11. This is not unlikely because

the markets for different bank products could be sharply different and the

largest banks in one market may not be necessarily so in other ones.

Finally, we compare concentration measures of Indian bankingindustry to those in a few other developed economies based on the

results of Bikker and Haaf (2001a). Bikker and Haaf (2001) observed

high market concentration in Denmark, Greece, Netherlands and

Switzerland and low market concentration in France, Germany, Italy,

Table 5 : Movement of Various Measures of

Concentration: 1989-90 to 2000-01

Year No. of GINI 1-Bank HHI HTI RI CCI CC Entropy

Banks Ratio

1990 75 0.757 0.281 0.103 0.058 0.055 0.328 0.767 3.057

1991 77 0.757 0.279 0.102 0.057 0.054 0.325 0.767 3.079

1992 77 0.750 0.278 0.101 0.055 0.052 0.324 0.760 3.105

1993 76 0.733 0.261 0.091 0.052 0.049 0.306 0.742 3.174

1994 74 0.721 0.256 0.089 0.050 0.048 0.301 0.731 3.192

1995 83 0.725 0.237 0.079 0.049 0.044 0.282 0.734 3.300

1996 92 0.738 0.241 0.079 0.047 0.041 0.283 0.746 3.340

1997 97 0.734 0.233 0.075 0.045 0.039 0.274 0.742 3.404

1998 100 0.734 0.226 0.072 0.045 0.038 0.267 0.742 3.437

1999 100 0.732 0.234 0.075 0.046 0.037 0.273 0.740 3.433

2000 100 0.729 0.236 0.074 0.045 0.037 0.273 0.736 3.441

2001 99 0.727 0.244 0.077 0.045 0.037 0.279 0.735 3.425


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Luxembourg and the US. Table 7 juxtaposes the HHIand CRk (fork=3, 5and 10) measures based on total assets for India along with similar measures

for 20 countries during the year 1997. It is interesting to observe that

market concentration in banking in India appears to be low as compared

to other countries. For example, India ranks joint 8th

(with Spain) with

respect to HHI and joint 6th

(with UK) with respect to CR3

measure.

Table 7: Concentration Indices for 21 Countries,

based on Total Assets: 1997

Countries HHI CR 3

CR5

CR10

No. of banks

Australia 0.14 0.57 0.77 0.90 31

Austria 0.14 0.53 0.64 0.77 78Belgium 0.12 0.52 0.75 0.87 79

Canada 0.14 0.54 0.82 0.94 44

Denmark 0.17 0.67 0.80 0.91 91

France 0.05 0.30 0.45 0.64 336

Germany 0.03 0.22 0.31 0.46 1803

Greece 0.20 0.66 0.82 0.94 22

India 0.08 0.34 0.43 0.62 97

Ireland 0.17 0.65 0.73 0.84 30

Italy 0.04 0.27 0.40 0.54 331

Japan 0.06 0.39 0.49 0.56 140

Luxembourg 0.03 0.20 0.30 0.49 118

Netherlands 0.23 0.78 0.87 0.93 45

Norway 0.12 0.56 0.67 0.81 35

Portugal 0.09 0.40 0.57 0.82 40Spain 0.08 0.45 0.56 0.69 140

Sweden 0.12 0.53 0.73 0.92 21

Switzerland 0.26 0.72 0.77 0.82 325

UK 0.06 0.34 0.47 0.68 186

US 0.02 0.15 0.23 0.38 717

Source : Except India, other figures have been taken from Bikker and Haaf (2001a).

Table 6 : Product Moment Correlations among DifferentMeasures of Concentration

GINI HHI RI CR1 CR2 CR3 CR4 CCI CC ENT

GINI 1.00

HHI 0.74 1.00

RI 0.66 0.97 1.00

CR 1 0.73 0.99 0.94 1.00

CR 2 0.76 0.99 0.97 0.99 1.00

CR 3 0.72 0.98 0.99 0.95 0.98 1.00

CR 4 0.70 0.95 0.99 0.91 0.95 0.99 1.00

CCI 0.73 0.99 0.97 0.99 0.99 0.98 0.95 1.00

CC 0.99 0.79 0.72 0.78 0.81 0.77 0.75 0.78 1.00

ENT -0.66 -0.98 -0.99 -0.95 -0.97 -0.99 -0.99 -0.98 -0.72 1.00


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II.3 Changes in the Market Structure in Indian Banking:Dynamic Measures

In the case of computing generalised entropy measures, the

permanent assets (deposits/income) are computed based on t

weights. These t

weights used here are the ratio of mean assets at

time t to the mean assets over the entire M periods. In our

computations, the substitutions parameter b is restricted by therelation -g = 1 + b. We computed four different aggregator functions

corresponding to four inequality measures with -g= V = (2, 1, 0.5,0.0). V = 0.0 and 1.0 correspond to Theils first and second inequality

measures, respectively, combined with the linear and the Cobb-Douglas forms of the aggregator function. Table 8 provides the annual

short-run inequalities, the inequalities in the aggregated (long-run)

assets, and the assets stability measures RM

. Decomposition of each

between and within groups is also presented.

Short-run inequality in Table 8 has generally decreased.

Surprisingly, the inequality has not become greater with larger degrees

of relative inequality aversion (V). For V other than 2, the within-group

component of short-run inequalities is dominant. However, the absolute

values of both within-group and between-group inequality measures

have recorded a significant decline over the 12 years period.

The long-run inequality has recorded relatively less volatility. In all

the years, the values of Ig(S) have decreased and in most cases

they are dominated by within-group component measures. In

some cases, the long-run inequality measures are higher than the

short-run component. It may be mentioned that these relative values

are somewhat sensitive to the size distribution. The corresponding

stability measures showed somewhat different pattern. Although,

the stability measures have fallen over the years, they have been

highly dominated by between-group component and the impactof within-group has been marginalised. Thus, there is a tendency

for the profiles of the banks to fall, and then level off in the years

to come. These patterns are robust with respect to the choice of

aggregation function, size-adjustment and inequality measure.


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The fact that the profiles are becoming flatter is an indication

that, although there have been some transitory movements in the

size distribution of assets, there is a lack of any permanent

equalization. Further more, while some equalisation has taken

place within each group of banks, inequality between groups has

been noticeably high.

Section III

Impact on Prices and Quantities

In this section, we examine the possible impact of the changes

in concentration on the prices and output in the banking sector. It

may be noted that as we have limited observations

Table 8: Empirical Values of Generalised Entropy MeasuresYear Short run Long run Stability

Overall Between Within Overall Between Within Overall Between Within

Degree of inequality aversion = 2.0

1990-93 2.028 0.891 1.137 2.068 0.904 1.164 1.020 1.014 0.006

1990-95 1.744 0.762 0.982 1.877 0.794 1.084 1.076 1.042 0.035

1990-97 0.959 0.641 0.319 1.243 0.696 0.547 1.296 1.086 0.210

1990-99 0.777 0.544 0.233 1.036 0.620 0.416 1.333 1.139 0.194

1990-01 0.696 0.475 0.221 0.904 0.564 0.340 1.299 1.187 0.1121996-01 0.552 0.354 0.198 0.581 0.362 0.219 1.053 1.022 0.030


1990-93 1.333 0.507 0.826 1.141 0.375 0.766 0.856 0.740 0.116

1990-95 1.254 0.460 0.794 1.074 0.348 0.727 0.856 0.755 0.102

1990-97 1.175 0.409 0.766 1.016 0.311 0.705 0.865 0.759 0.1051990-99 1.134 0.364 0.770 0.982 0.276 0.706 0.866 0.758 0.108

1990-01 1.112 0.328 0.784 0.972 0.246 0.726 0.874 0.750 0.124

1996-01 1.052 0.273 0.780 0.956 0.232 0.724 0.909 0.852 0.057


1990-93 1.068 0.425 0.643 1.063 0.422 0.641 0.995 0.993 0.002

1990-95 1.017 0.394 0.623 1.008 0.387 0.621 0.991 0.982 0.009

1990-97 0.966 0.357 0.609 0.955 0.344 0.611 0.989 0.965 0.024

1990-99 0.936 0.323 0.613 0.921 0.305 0.616 0.984 0.945 0.0391990-01 0.921 0.294 0.627 0.905 0.272 0.633 0.983 0.927 0.055

1996-01 0.879 0.251 0.628 0.879 0.247 0.632 1.000 0.981 0.019


1990-93 1.147 0.438 0.709 1.331 0.506 0.825 1.160 1.155 0.006

1990-95 1.093 0.397 0.696 1.262 0.458 0.804 1.155 1.153 0.0011990-97 1.041 0.358 0.683 1.194 0.405 0.789 1.147 1.132 0.015

1990-99 1.011 0.333 0.678 1.143 0.358 0.785 1.131 1.075 0.0551990-01 0.997 0.324 0.673 1.115 0.320 0.795 1.118 0.988 0.130

1996-01 0.955 0.303 0.652 1.063 0.271 0.792 1.113 0.893 0.220


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(e.g., annual data only), the causal nature of market structure andperformance is difficult to establish. It is well known that even

in a market structure that is apparently monopolistic, competitive

prices may exist due to threat of entry. Our arguments in this

section are, therefore, not definitive. However, despite limitations,

our observations in this section may turn out to be useful in

reconciling the conceptual anomalies and as a consequence, in

forming the suitable hypotheses.

The literature that discusses the relationship between market

structure and competitiveness is voluminous. The survey of

Bikker and Haaf (2001a) also covers this area, focussing ondifferent theoretical and empirical approaches with special

reference to banking. To link concentration and competitiveness

empirically, one needs to specify and estimate appropriate models

based on panel data.1 2 In the panel data models, disaggregated

bank specific data on some performance measures is regressed

on the banks own market share, market concentration at the

aggregate level and other control factors. The performance

measures are typically based on profits or prices. While data on

prof it s are taken from the prof it and loss accounts of banks,

appropriate bank specific interest rates are supposed to be a proxy

for the prices. Empirical findings suggest monopolisticcompetition; competition appears to be weaker in the local

markets and stronger in the international markets. The relationship

for the impact of market structure on competition seems to support

the conventional view that concentration impairs competitiveness.

It may be noted that whatever be the theoretical structure specified,

the empirical measures for prices in banking are not very clear. As

there is no clear common methodology for measuring prices and output

of financial intermediation services and SNA 1993 recognizes this as a

problem area, Subsection III.1 discusses a few common conceptual

problems in the literature, and in this context, emphasises that the direct

use of select interest rates as a price measure may not be conceptually

appropriate. Arguing on the basis of the user cost approach, we suggest

the use of spread as price measures for banking. Subsection III.2 reviews

alternative empirical estimates of prices and output for this sector


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in India during the reference period, and attempts to relate it to ourearlier findings. In particular, it compares inflation measures for banking

based on traditional GDP deflators and spread, and finds the latter to be

more consistent with the changing patterns of market structure of banking

in India.

III.1 Conceptual Problems in Measurement of Prices and

Output of Financial Intermediation Services

It may be noted that measurements of prices and output of

services are difficult because services are produced and consumed atthe same point of time. Also, prices of services are more dispersed

across regions because of their non-tradable nature (Grilliches, 1992).

Besides these common problems, measurements of prices and outputof financial services are further limited due to many conceptual

problems that have not yet been resolved satisfactorily. First, it is

not clear whether financial services are attached to the financialinstruments, accompanying the transactions or to the monetary units

being transacted. Most of the activities of a bank involve processing

documents (such as cheques and loan payments) and dealing withcustomers (Benston et al., 1982). Consequently, previous researchers

have used the average number of deposit and loan accounts serviced

per month as their unit of output to measure the customer relatedservices. Alternatively, Fixler (1993) has argued that the amount of

financial services sold by a bank can be more appropriately measured

by the money balances in the various products. Secondly, it isnot clear which financial services are relevant to the measurement

of output: those attached to assets, liabilities or both? This

question concerns the precise identification of inputs and outputs.The debate on measurement of bank output mainly revolves around

the status of demand deposit related financial services . Demand

deposits have the characteristics of both input and output. Onone hand, they are like raw materials in the financial

intermediation process and are used for production of loans andinvestment; on the other hand, a host of final services(e.g. , maintenance of money, free cheque facilities, etc.) are

attached to them. Till now, consensus regarding the status of

demand deposit has not emerged in the literature. Thirdly, many of


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the financial services are jointly produced with a sequence of bartertransactions and are typically assigned to a bundle of financial

services, the pricing of which is difficult and is often apparently

free in nature.

These conceptual problems imply that any measurement of the

services provided by banks in real terms would be difficult. In the

absence of precise measures of prices and output in banking,researchers have attempted to resolve the problem indirectly by

developing certain indicators either for production or for the prices.

A few common indicators have been used widely in official statistics,

for conversion of value added of the banking sector from currentprices to constant prices. In many cases, the indicators have focused

on a single aspect related to the sector, concentrating on a simple

ratio-variable and hoping that other related variables move

proportionally to the one proposed.

Till the end of 1980s, the United States (US) Bureau of

Economic Analysis (BEA) used one such indicator for conversion of

gross product originating (GPO) in the banking sector from current

prices to constant prices. To do that, the benchmark value of GPO at

current prices was determined for a particular year. Output for

subsequent years was calculated by extrapolating the benchmark valueby a factor based on the number of persons engaged in production,

the implicit assumption in the method being that there had not been

any growth in labour productivity in banking! When applied, the

estimates showed very small real output growth in the banking sector,

so small that many economists believed that the method

underestimated real output of the banking sector in the US economy

(Fixler, 1993). So far as the other countries are concerned, it is also

not uncommon to find the movement of value added at constant prices

estimated by means of changes in the compensation of employees at

constant prices (SNA 93, page 397).

The conversion factor used in the National Accounts Statistics

(NAS) in India is slightly different. In India, the base year estimates of

value added from the banking sector are carried forward using an indicator

based on the ratio of aggregate deposits for the two years and the


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wholesale price index (WPI). The volume of activity is measured invalue terms, the indicator being the ratio of aggregate deposits. To obtain

the quantity index, the ratio of deposits for two years is deflated by

WPI.

It may be noted that the quantity index of banking used in the

NAS in India covers only one aspect of banking, i.e., deposits; other

aspects like credit are totally neglected. This may turn out to be a

serious limitation because the different products of banks are fairly

heterogeneous in nature. A composite index based on activities of a

bank would perhaps be more preferable. Moreover, deflation by WPI

to derive the quantity index is tantamount to the assumption thatprices for banking move parallel to that of the goods sector as a

whole, which may not be valid in reality.

Besides these simple indicators, models of real banking activity

and measures for prices and quantities of various products offered

by banks have also been developed in the bank regulation literature. To

determine whether economies of scale exist in banking, researchers have

estimated explicit multioutput production or cost functions. Typically,

such functions include bank financial inputs and outputs, and the usual

capital, labour and material inputs. Though precise measures of nominal

and real outputs are absolutely crucial for such studies, a variety ofapproaches have been followed, and a consensus on conceptual questions

has not yet emerged.

In the literature, three distinct approaches, viz., the asset

approach, value added approach and user cost approach, are available.

The process of generation of output and the role of demand deposit

in all these three approaches are sharply different. Each of these

approaches has certain advantages and certain drawbacks and

adoption of any one of them depends on the objective of the study. A

detailed discussion on all these approaches is beyond the scope of

the paper.1 4

The paper restricts its attention on the user cost approach because a

major focus in this approach is on measuring the implicit prices of

financial intermediation through user cost (Hancock, 1985; Fixler and


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Zieschang, 1992; Fixler, 1993). The user cost approach attempts tomeasure prices of financial intermediation from the interest rates of

different financial instruments. In traditional applications, prices of

different financial instruments have been measured as deviations of the

rate of return associated with them from a benchmark risk-free financial

instrument (e.g., discount rates of treasury bill, coupon rates of standard

Government bonds, bank rates, etc.). But the problem with this approach

is that the estimates provided by it would be crucially related to the

profitability of the banking sector. If the risk of default is high, banks

might not be willing to disburse more credit as the amount disbursed

might turn into a non-performing asset (NPA). If NPAs of banks increase,

the effective returns from these assets would decrease. In such situations,the banks might tend to allocate a substantial portion of their funds inapproved securities. Thus, if the profitability of the banking sector

decreases, returns from advances would become closer to the return fromthe benchmark rates and for some periods, it might be less than theserates leading to zero or negative prices for some instruments.Alternatively, the weighted average rates of all asset and liability products

of the banking sector have also been considered as the standard rate.

In the Indian context, Srimany and Bhattacharya (1998) have

obtained empirical estimates based on traditional user cost approach

and compared the results with alternative estimates. Samanta andBhattacharya (2000), on the other hand, highlight the role of spread

in this context. Their study demonstrates that under some simplifying

conditions, the spread between rates of interest charged by the bank

to borrowers and depositors could be given the interpretation of a

price for financial intermediation.

III.2 Empirical Estimates of Prices and Output of Banking

Services in India

In this section, we examine the behaviour of price and output of

the banking sector from the national income data. Figure 1 presents thenominal (Chart 1a) and the real (Chart 1b) growth rates in GDP from the

banking sector during the period under study. To compare the sectors

relative performance, these figures have been juxtaposed with the overall

nominal and real growth of GDP in the respective parts. In Chart 1a, the


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curves of NGDPGr and NBnkGr reflect the nominal overall GDP

growth and GDP growth pertaining to the banking sector, respectively.

Similarly, In Chart 1b, the curves of RGDPGr and RBnkGr show the

corresponding values in real terms.

Using the nominal and real GDP figures, the implicit deflatorsin banking as well as for the entire economy could be obtained. Thesedeflators could be used to obtain measures pertaining to sectoral and

overall inflation. Chart 2 presents the implicit inflation in bankingservices as per the NAS in India (Inf_Bnk). Once again, to compareits relative performance, these figures have been juxtaposed with (i)the overall inflation in all commodities and services as measuredby the GDP deflator (Inf_GDP), and (ii) inflation based on WPI(Inf_WPI).

It may be noted that the method adopted by India in preparingNational Accounts Statistics is fully consistent with the internationalconventions. Given this, the above figures look strange. While it isexpected that the growth rates in a services sector may be erratic andmay fluctuate from year to year, such high fluctuation reveals the

general methodological weakness in the convention adoptedinternationally. Is it possible that when inflation rates in almost all

the sectors in an economy are on a declining trend, the banking sector

experiences a more than 20 per cent rise in the prices and in the very

next year experiences a deflation? We argue that the NAS is providing


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a misleading picture because there is circularity in measurement of banking

services. The circularity occurs because of the use of WPI to deflate the

nominal figures of the value added in the banking services, and that too

based on solely the movements in deposits.

As an alternative, we examine the movements in spread-basedmeasures. Although the theoretical implications of spread have beenexamined in detail, there is no unique empirical definition of spread.

Brock and Rojas-Saurez (2000) have suggested six alternative proxiesfor banks spread, ranging from a narrow concept one that includes


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loans and investments in the assets side and deposits and borrowings inthe liability side to a broad concept, where all interest earning assets

and interest bearing liabilities plus associated fees and commissions are

included. In addition, one may perhaps consider the simple difference

of a standard lending rate and the deposit rate as a proxy. In the Indian

context, some of these measures have been computed and examined,

sometimes separately across bank groups. For example, Chapter VII of

the Report on Currency and Finance (1999-2000) defines spread as net

interest income to total assets and computes this measure separately across

bank groups from 1991-92 to 1999-00. The Report observes a gradual

reduction of spread and attributes this reduction to competitive pressures.

The Report also observes a tendency towards their convergence acrossall bank-groups, except foreign banks (pp. VII-1)

In addition to the measures suggested by Brock and Rojas-Saurez

(2000), in this paper, we have considered three additional measures based

on the simple differences between lending and deposit rates. As the

interest structure during the early 1990s in India was administered, these

measures are expected to reveal the extent of administered spread in

India during the same period. The definitions of these measures are

presented in Table9.

Table 10 presents estimates pertaining to the nine alternative meas-ures of spread from 1989-90 to 2001-02. From Table 10, it is observed

that there are strong correlations among many of the pairs of measures

for spread. In general, correlations for pairs within a broad group are

high and sometimes more than 0.95. However, in general correlations

for pairs in different groups are moderate. Interestingly, measures in

Group 3 have negative correlations with measures in other groups.

A detailed examination of these measures reveals that they are in

general agreement with the observations of Reserve Bank of India.

Almost all the measures display a decreasing trend during the second

half of the 1990s. Thus, the spread appears to have decreased, implyinga change in the price for financial intermediation. In this context, it may

be noted that as during the early 1990s, interest rates in India were

administered, the measures for spread during these periods may not reflect

market forces properly and thus, may not be consistent with the existing


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Table 9: Alternative Definitions of SpreadGroup 1

SPN1= [(interest earned on advances/advances)(interest paid on deposits/

deposits)]* 100;

SPN2 = [(interest earned on advances/advances)(interest paid on deposits and

borrowings)/(deposits+borrowings)]* 100;

SPN3 = [(interest earned on advances and investments)/(advances+investments)

(interest paid on deposits and borrowings)/(deposits+borrowings)]* 100;

Group 2

SPB1 = (interest earned interest paid)/(total assets)*100;

SPB2 = [{(interest earned)/(interest earning assets 15)}

{(interest paid)/(interest bearing liabilities16 )}]*100;SPB3 = [{(interest earned +commission, exchange and brokerage)/

(interest earning assets)} {( interest paid)/(interest bearing

liabilities)}]* 100;

Group 3

SPI1 = Lending Rate Time Deposit Rate for Less Than One YearMaturity

SPI2 = Lending Rate Time Deposit Rate for One to Three Years Maturity

SPI3 = Lending Rate Time Deposit Rate for Beyond Five Years Maturity

Table 10 : Alternative Measures of Spread in the

Banking Sector in India: 1989-90 to 2001-02

Year SPN1 SPN2 SPN3 SPB1 SPB2 SPB3 SPI1 SPI2 SPI3

1989-90 1.78 2.46 3.28 7.00 6.50 6.50

1990-91 1.77 2.43 3.32 7.00 5.50 5.50

1991-92 6.27 5.57 3.95 3.31 4.14 4.99 4.50 3.50 3.50

1992-93 4.81 4.63 3.67 2.50 3.48 4.33 8.00 8.00 8.00

1993-94 5.22 5.14 3.71 2.54 3.32 4.22 9.00 9.00 9.00

1994-95 4.25 4.24 4.37 3.01 3.65 4.59 4.00 4.00 4.00

1995-96 5.43 5.37 4.90 3.15 3.82 4.85 4.50 3.50 3.50

1996-97 6.29 6.29 4.86 3.22 3.85 4.82 3.00 2.00 1.75

1997-98 4.60 4.60 4.21 2.95 3.38 4.28 3.25 2.25 2.25

1998-99 4.19 4.26 3.85 2.78 3.15 4.00 3.00 2.00 2.00

1999-00 3.59 3.61 3.58 2.72 3.13 3.94 3.00 1.75 1.75

2000-01 3.74 3.78 3.62 2.84 3.18 3.91 2.75 1.75 1.75

2001-02# 3.05 3.22 3.15 2.81 3.10 3.82 3.25 2.87 2.87

# : Provisional for SPI1, SPI2 and SPI3.

market structure. However, it is interesting to observe that from 1995-

96 onwards, all the measures of spread pertaining to the first two groups

reveal a strong downward trend. Thus, it is logical to argue that as soon

as the administered price regime in banking in India was lifted, the


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favourable market structure put a downward pressure on the pricesthrough competition. The result once again establishes that a favourable

market structure alone may not be adequate for competitive prices and

other institutional features and policy measures also contribute

significantly towards it.

Section IV

Conclusion

The paper examined the nature and the extent of changes in the marketconcentration in the Indian banking sector and their possible implicationson competitiveness, prices and outputs of banking services. The paperwas logically divided into two parts. The first part measured marketconcentration in banking in India in alternative ways from 1989-90 to2000-01. In contrast to earlier empirical applications on banking, this paperfocussed on both static and dynamic measures of market concentration.The paper found strong evidence of change in market structure in bankingin India. Interestingly, results reveal that a major part of the change inmarket structure occurred during the early 1990s. Despite a spate of mergersduring the late 1990s, banking market concentration in India was notsignificantly affected. It was also observed that different concentrationratios rank the changes of banking market concentration in India similarlyover time. This result, in conjunction with Bikker and Haaf (2001a),

provides evidence that despite the existence of a host of concentrationmeasures, empirical rankings of countries over space or time may not besignificantly affected due to differences in measures used.

The second part of the paper analysed the possible impact of changesin banking market structure on prices and output during the same period.It was articulated that before measuring competitiveness, the fuzzy issuesrelating to measurements of prices and quantities of banking servicesneeded to be satisfactorily resolved. Using Indian data, the paperdemonstrated that measurement problem of real output pertaining tobanking sector in the national income data could be severe. The impliedinflation as obtained through the GDP deflator for the banking sector in

India led to unbelievable measures of inflation for banking services, castingsome doubt on the methodology adopted. Alternatively, proxy pricemeasures based on spread appeared to be more consistent with the changesin market structure in India during the late 1990s. The paper arguedthat the favourable market structure in India could be one importantfactor that led to a reduction in the prices of banking services after


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the administered interest regime was lifted. Although it might be wrongto attribute the entire change in spread to the change in market structure,it was logically one important factor that could lead to lagged reductionin the prices of banking services in a favourable environment, freedfrom arbitrary price restrictions. A deeper study addressing theseproblems in a cross-country perspective would thus , be useful in

narrowing the current gaps in the literature.

Notes

1 For example, the Report on Currency and Finance (1998-99) published by the Reserve

Bank of India reports some of these indices and comments on the nature of concentration

of export of the Indian economy (pp.IX.6).

2 For example, the survey of Bikker and Haaf (2001a) demonstrates that for 20 countries,

the rankings of the k-Bank Concentration Ratios and the HHI are strongly correlated.

3 In the US, the Department of Justice, uses HHI to assess whether mergers and acquisitions

significantly constitute a threat to competition.

4 This is particularly so in the case of econometric studies of wage dispersion in which

statistical causes of dispersion are usefully identified, but welfare-theoretic motivation is

lacking with regards to dispersion as a measure of mobility, or inequality.

5 In other work with PSID data, Maasoumi and Zandvakili (1990) have studied different

weights, including Principal Component weights and unequal subjective weights. They

find these weights are inconsequential for the qualitative inferences and rankings.

6 The taxonomy, in detail, is available in the Report on Trend and Progress of Banking inIndia, published by the Reserve Bank of India (RBI), for different years. These Repor ts

also analyse the implications of the major developments in the Indian banking market in

detail.

7 See the Box II.1 entitled Merges and Acquisition in Banking: International Experiences

and Indian Evidence in the Report on Trend and Progress of Banking in India 2000-01

(pp. 5152) by the Reserve Bank of India for details.

8 The results based on other indicators at industry as well as bank-group level are available

with the authors.

9 See Bikker and Haaf (2001a) for details

10 The rank-correlations among various indices also show similar results.

11

Results are available with the authors and can be obtained on request.12 The structural approach to model competition includes Structure-Conduct-Performance (SCP)

paradigm and efficiency hypothesis. The SCP paradigm investigates, whether a highly

concentrated market causes collusive behaviour among large banks resulting in superior market

performance; whereas efficiency hypothesis tests, whether it is the efficiency of larger banks

that makes for enhanced performance. On the other hand, non-structural models


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like Panzar and Rosse (P-R) model, uses explicit information about the structure of the

market.

13 These types of indices have been applied to measure productivity of the banking sector. U.S.

measures of banking labor productivity adopt the activity approach bank output includes counts

of loan and deposit activities (such as loan applications processed and cheques cleared).

14 For a recent survey of literature, see Srimany and Bhattacharya (1998).

15 Interest earning assets include advances, investments, balances with central bank, balances

with other banks and money at call and short notice.

16 Interest bearing liabilities include deposits and borrowings.

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Dynamics of Market Structure